Known is an automation apparatus for developing and designing industrial facilities, particularly for developing, designing, realizing, bringing into service, maintaining, and optimizing individual components of a facility or packaged facilities (cf., RF Patent No. 2,213,365, Siemens AG, DE), that provides computer-based creation of mathematical and physical models of a process, neuron network models, and knowledge base systems, wherein decentralized execution and optimization of the process are carried out by one or more control stations that communicate with each other to form a control station network using modern communications means. The apparatus comprises a computer, control stations that communicate with each other by communications means in the form of a telephone, digital communications, satellite or Internet/Intranet communications network, that are embodied as technological central control stations spaced from the plant and communicating with a control system of an industrial facility by means of remote data transmission.
The disadvantage of said apparatus is control and optimization of individual plants that are small-dimensionality systems.
The closest technical solution is a power plant control system consisting of a plurality of power units (cf., RF Patent No. 2,138,840, Siemens AG, DE), said system comprising a computing unit, an optimization module connected thereto via data lines and connected to a plurality of neuron networks, wherein the computing unit is designed to determine driving values for that or other power unit of a power plant using a genetic algorithm, and each power unit is connected to a respective neuron network and to the computing unit via the data lines.
The computing unit determines a specified value for a power fraction of each power unit in the total load to be covered as a driving value F for said power unit for a specified time interval. The optimization module comprises a rough optimization stage and a fine optimization stage both connected to neuron networks, wherein the fine optimization stage automatically models a process. There is an individual neuron network to generate start values for the genetic algorithm of the computing unit as well.
The disadvantage of the present control system is the insufficient speed of operation in solution of problems of optimizing production processes in large industrial systems because of necessity to transmit all input information of characteristics of control objects included in the system to a central control apparatus comprising the computing unit and the optimization module associated therewith. Reverse transmission of solution results from said central apparatus to all controllable objects included in the system is necessary as well.
Thus, total volume of information to be transmitted for a large system of significant spatial extension is very large.
Another disadvantage of the prior art control system is that the case of solving a problem of optimizing an operation mode of a large system including a great number of power units and being described by a system of high-order equations stipulated a sequential mode of executing necessary computations in the optimization module of said system. When the dimensionality of a problem to be solved is large and a number of iterations necessary to solve said problem according to the mode optimization algorithm is significant, the volume of computations is bulky and a significant time is required to solve the present problem.